• Chinese Optics Letters
  • Vol. 21, Issue 4, 040601 (2023)
Fanran Meng, Wenxiang Zhang, Xiaojun Liu, Fei Liu*, and Xian Zhou
Author Affiliations
  • School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
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    DOI: 10.3788/COL202321.040601 Cite this Article Set citation alerts
    Fanran Meng, Wenxiang Zhang, Xiaojun Liu, Fei Liu, Xian Zhou. Comparative analysis of temporal-spatial and time-frequency features for pattern recognition of φ-OTDR[J]. Chinese Optics Letters, 2023, 21(4): 040601 Copy Citation Text show less
    Experimental setup of φ-OTDR. (a) Knocking with a hammer; (b) wind blowing. AOM, acoustic-optic modulator; EDFA, erbium-doped fiber amplifier; LO, local oscillator; BPD, balanced photodetector; DAQ, data acquisition card.
    Fig. 1. Experimental setup of φ-OTDR. (a) Knocking with a hammer; (b) wind blowing. AOM, acoustic-optic modulator; EDFA, erbium-doped fiber amplifier; LO, local oscillator; BPD, balanced photodetector; DAQ, data acquisition card.
    (a) Developed prototype φ-OTDR instrument; (b) deployed fiber in the experiment.
    Fig. 2. (a) Developed prototype φ-OTDR instrument; (b) deployed fiber in the experiment.
    Flow chart of DSP.
    Fig. 3. Flow chart of DSP.
    Time-domain waveform of vibration signal. (a) Knock around the fiber with a hammer; (b) wind blowing; (c) background noise.
    Fig. 4. Time-domain waveform of vibration signal. (a) Knock around the fiber with a hammer; (b) wind blowing; (c) background noise.
    Temporal-spatial image and time-frequency image for vibration events. (a), (d) Knock around the fiber with a hammer; (b), (e) wind blowing; (c), (f) background noise.
    Fig. 5. Temporal-spatial image and time-frequency image for vibration events. (a), (d) Knock around the fiber with a hammer; (b), (e) wind blowing; (c), (f) background noise.
    Network structure of ResNet50.
    Fig. 6. Network structure of ResNet50.
    Residual blocks of ResNet50.
    Fig. 7. Residual blocks of ResNet50.
    (a) Classification accuracy curve and (b) loss curve of training.
    Fig. 8. (a) Classification accuracy curve and (b) loss curve of training.
    Confusion matrix of (a) temporal-spatial image and (b) time-frequency image.
    Fig. 9. Confusion matrix of (a) temporal-spatial image and (b) time-frequency image.
    Event TypeKnockingBlowingNoise
    Temporal-spatial training set599804888
    Temporal-spatial validation set421396312
    Total102012001200
    Time-frequency training set845804641
    Time-frequency validation set355396379
    Total120012001020
    Table 1. Composition of the Data Set
     PrecisionAccuracyAverage Training Time/s
    KnockingNoiseBlowing
    Temporal-spatial image99.64%98.98%99.98%99.49%219
    Time-frequency image95.51%99.18%98.19%98.23%216
    Table 2. Comparison of Training Results between Temporal-Spatial and Time-Frequency Images
    Event TypeRecallf1-Score
    Temporal-spatialKnocking99.76%99.76%
    Noise99.36%99.52%
    Blowing99.75%99.62%
    Time-frequencyKnocking99.15%97.50%
    Noise96.04%97.59%
    Blowing100%100%
    Table 3. Comparison of Recall and f1-Score
    Fanran Meng, Wenxiang Zhang, Xiaojun Liu, Fei Liu, Xian Zhou. Comparative analysis of temporal-spatial and time-frequency features for pattern recognition of φ-OTDR[J]. Chinese Optics Letters, 2023, 21(4): 040601
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